Different approaches to manually create Spark DataFrames

Matthew Powers
2 min readMay 22, 2017

This blog post explains the Spark and spark-daria helper methods to manually create DataFrames for local development or testing.

We’ll demonstrate why the createDF() method defined in spark-daria is better than the toDF() and createDataFrame() methods from the Spark source code.

See this blog post if you’re working with PySpark (the rest of this post uses Scala).


toDF() provides a concise syntax for creating DataFrames and can be accessed after importing Spark implicits.

import spark.implicits._

The toDF() method can be called on a sequence object to create a DataFrame.

val someDF = Seq(
(8, "bat"),
(64, "mouse"),
(-27, "horse")
).toDF("number", "word")

someDF has the following schema.

| — number: integer (nullable = false)
| — word: string (nullable = true)

toDF() is limited because the column type and nullable flag cannot be customized. In this example, the number column is not nullable and the word column is nullable.

The import spark.implicits._ statement can only be run inside of class definitions when the Spark Session is available. All imports should be at the top of the file before the class definition, so toDF() encourages bad Scala coding practices.

toDF() is suitable for local testing, but production grade code that’s checked into master should use a better solution.


The createDataFrame() method addresses the limitations of the toDF() method and allows for full schema customization and good Scala coding practices.

Here is how to create someDF with createDataFrame().

val someData = Seq(
Row(8, "bat"),
Row(64, "mouse"),
Row(-27, "horse")

val someSchema = List(
StructField("number", IntegerType, true),
StructField("word", StringType, true)

val someDF = spark.createDataFrame(

createDataFrame() provides the functionality we need, but the syntax is verbose. Our test files will become cluttered and difficult to read if createDataFrame() is used frequently.


createDF() is defined in spark-daria and allows for the following terse syntax.

val someDF = spark.createDF(
(8, "bat"),
(64, "mouse"),
(-27, "horse")
), List(
("number", IntegerType, true),
("word", StringType, true)

createDF() creates readable code like toDF() and allows for full schema customization like createDataFrame(). It’s the best of both worlds.

Big shout out to Nithish for writing the advanced Scala code to make createDF() work so well.

Creating Datasets

Datasets are similar to DataFrames, but preferable at times because they offer more type safety.

See this blog post for explanations on how to create Datasets with the toDS and createDataset methods.

Including spark-daria in your projects

The spark-daria README provides the following project setup instructions.

  1. Update your build.sbt file.
libraryDependencies += "com.github.mrpowers" %% "spark-daria" % "0.38.2"

2. Import the spark-daria code into your project:

import com.github.mrpowers.spark.daria.sql.SparkSessionExt._

Closing Thoughts

DataFrames are a fundamental data structure that are at the core of my Spark analyses.

See this blog post for the different approaches on how to create Datasets, a related data structure.

I wrote a Beautiful Spark Code book that teaches the core aspects of Spark development with DataFrames. The book is the best way to learn how to get good at Spark quickly.



Matthew Powers

Spark coder, live in Colombia / Brazil / US, love Scala / Python / Ruby, working on empowering Latinos and Latinas in tech